How do you solve Binary Tree Postorder Traversal on LeetCode?

Binary Tree Postorder Traversal (LeetCode #145) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Postorder traversal visits nodes in the order of left-right-root.

What is the brute force solution for Binary Tree Postorder Traversal?

The brute force approach for Binary Tree Postorder Traversal is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach uses recursion to traverse the tree in postorder. We visit the left subtree, then the right subtree, and finally the root node, collecting values along the way. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Binary Tree Postorder Traversal?

Binary Tree Postorder Traversal uses the following concepts: Stack, Tree, Depth-First Search, Binary Tree. The recommended patterns to study are: Depth-First Search (DFS), Stack-based traversal.

What are the interview tips for Binary Tree Postorder Traversal?

Always clarify whether the traversal should be iterative or recursive when asked. Discuss your thought process and approach before jumping into coding. Consider edge cases, like empty trees or single-node trees, in your solution.

What are common mistakes when solving Binary Tree Postorder Traversal?

Forgetting to reverse the result in the iterative approach. Not handling null nodes properly, leading to null pointer exceptions.

#145

Binary Tree Postorder Traversal

Easy

StackTreeDepth-First SearchBinary TreeDepth-First Search (DFS)Stack-based traversal

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

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Intuition

Time O(n)Space O(n)

The optimal solution uses an iterative approach with a stack to simulate the postorder traversal without recursion. This avoids the overhead of recursive calls and allows us to manage the traversal more efficiently.

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Algorithm

5 steps

1Step 1: Initialize an empty stack and a result list.
2Step 2: Push the root node onto the stack.
3Step 3: While the stack is not empty, pop a node from the stack and add its value to the result list.
4Step 4: Push the left child first, then the right child onto the stack (if they exist).
5Step 5: Reverse the result list before returning it, as we collected values in root-right-left order.

solution.py12 lines

1def postorderTraversal(root):
2    if not root:
3        return []
4    stack, result = [root], []
5    while stack:
6        node = stack.pop()
7        result.append(node.val)
8        if node.left:
9            stack.append(node.left)
10        if node.right:
11            stack.append(node.right)
12    return result[::-1]

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Complexity note: The time complexity is O(n) because we visit each node exactly once. The space complexity is O(n) due to the stack storing nodes in the worst case (for a completely unbalanced tree).

1Postorder traversal visits nodes in the order of left-right-root.
2Using a stack allows us to simulate recursion and control the order of node processing.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.